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Purpose – The purpose of this paper is to propose a set of techniques, in the domain of texture analysis, dedicated to the classification of industrialtextures. One of the main purposes was to deal with a high diversity of textures, including structural and highly random patterns.Design/methodology/approach – The global system includes a texture segmentation phase and a classification phase. The approach for imagetexture segmentation is based on features extracted from wavelets transform, fuzzy spectrum and interaction maps. The classification architecture usesa fuzzy grammar inference system.Findings – The classifier uses the aggregation of features from the several segmentation techniques, resulting in high flexibility concerning thediversity of industrial textures. The resulted system allows on-line learning of new textures. This approach avoids the need for a global re-learning of theall textures each time a new texture is presented to the system.Practical implications – These achievements demonstrate the practical value of the system, as it can be applied to different industrial sectors forquality control operations.Originality/value – The global approach was integrated in a cork vision system, leading to an industrial prototype that has already been tested.Similarly, it was tested in a textile machine, for a specific fabric inspection, and gave results that corroborate the diversity of possible applications. Thesegmentation procedure reveals good performance that is indicated by high classification rates, revealing good perspectives for full industrialization

A texture segmentation prototype for industrial inspection applications based on fuzzy grammar